Not-For-All-Audiences

Instructions to use LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2

SGLang

How to use LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2
```

Model Card: ShoriRP-v0.57-merged

This is a merge between:

Mistral-7B-Instruct-v0.2
ShoriRP-v0.57 at a weight of 1.00.

The merge was performed using mergekit.

The intended objective was to make a controlled test merge at a weight of 1.00

Configuration

The following YAML configuration was used to produce this model:

merge_method: passthrough
models:
  - model: F:\AI\models\Mistral-7B-Instruct-v0.2+F:\AI\loras\ShoriRP-v0.57
dtype: float16

Usage

Please see the Lora repository for proper usage. All the prompt formatting JSONs are included in this repo for your convenience.

Bias, Risks, and Limitations

The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.

Training Details

This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.

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Model tree for LoneStriker/ShoriRP-merged-v0.57-3.0bpw-h6-exl2

Base model

mistralai/Mistral-7B-Instruct-v0.2

Finetuned

(1096)

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